CORE-Acu: Structured Reasoning Traces and Knowledge Graph Safety Verification for Acupuncture Clinical Decision Support

CORE-Acu: Structured Reasoning Traces and Knowledge Graph Safety Verification for Acupuncture Clinical Decision Support | Signal Canvas | ScienceToStartup

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Signal Canvas proof surface

Canonical route: /signal-canvas/core-acu-structured-reasoning-traces-and-knowledge-graph-safety-verification-for-acupuncture-clinical-decision-support

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID core-acu-structured-reasoning-traces-and-knowledge-graph-safety-verification-for-acupuncture-clinical-decision-support | Route /signal-canvas/core-acu-structured-reasoning-traces-and-knowledge-graph-safety-verification-for-acupuncture-clinical-decision-support

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/core-acu-structured-reasoning-traces-and-knowledge-graph-safety-verification-for-acupuncture-clinical-decision-support

MCP example

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  }
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source_context

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Evidence Receipt

Route status: building

Claims: 7

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: CORE-Acu: Structured Reasoning Traces and Knowledge Graph Safety Verification for Acupuncture Clinical Decision Support

PDF: https://arxiv.org/pdf/2603.08321v1

Source count: Pending verification

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

Signal Canvas receipt window

Watch and verify: CORE-Acu: Structured Reasoning Traces and Knowledge Graph Safety Verification for Acupuncture Clinical Decision Support

/buildability/core-acu-structured-reasoning-traces-and-knowledge-graph-safety-verification-for-acupuncture-clinical-decision-support

Watchwatch

Subject: CORE-Acu: Structured Reasoning Traces and Knowledge Graph Safety Verification for Acupuncture Clinical Decision Support

Verdict

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Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
we propose CORE-Acu, a neuro-symbolic framework for acupuncture clinical decision support that integrates Structured Chain-of-Thought (S-CoT) with knowledge graph (KG) safety verification.
Implicationpartial
This is the core technical approach described in the abstract and title.
Verificationpartial
partial
Evidencepartial
Crucially, CORE-Acu achieved 0/1,000 observed safety violations (95% CI: 0--0.37%), whereas GPT-4o exhibited an 8.5% violation rate under identical rules.
Implicationpartial
This is a direct and specific quantitative result presented in the abstract and analysis.
Verificationpartial
partial
Evidencepartial
Finally, we introduce the Lexicon-Matched Entity-Reweighted Loss (LMERL), which corrects terminology drift caused by the frequency--importance mismatch in general optimization by adaptively amplifying gradient contributions of high-risk entities during fine-tuning.
Implicationpartial
This specific technical component is clearly described as a contribution in the abstract.
Verificationpartial
partial
Evidencepartial
By enforcing an explicit causal chain from pattern identification to treatment principles, treatment plans, and acupoint selection, we transform implicit Traditional Chinese Medicine (TCM) reasoning into interpretable generation constraints, mitigating the opacity of LLM-based CDS.
Implicationpartial
This describes a key benefit and mechanism of the proposed framework.
Verificationpartial
partial
Evidencepartial
The market for clinical decision support systems is growing rapidly, especially with increasing integration of traditional medicine practices in modern healthcare. Acupuncture practitioners and integrative medicine centers could benefit by paying subscription fees for more reliable and compliant practice.
Implicationpartial
This claim is derived from the 'product_opportunity' section, indicating market potential.
Verificationpartial
partial
Evidencepartial
Also, the dependence on manual dataset annotations and expert validation might raise scalability issues.
Implicationpartial
This is explicitly stated as a caveat in the 'caveats' section of the analysis.
Verificationpartial
partial
Evidencepartial
Furthermore, we construct a TCM safety knowledge graph and establish a ``Generate--Verify--Revise'' closed-loop inference system based on a Symbolic Veto Mechanism, employing deterministic rules to intercept hallucinations and enforce hard safety boundaries.
Implicationpartial
This describes a core component of the system's inference process and safety mechanism.
Verificationpartial
partial

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CORE-Acu: Structured Reasoning Traces and Knowledge Graph Safety Verification for Acupuncture Clinical Decision Support

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